Category Archives: Artificial intelligence

AI, ambit software, Artificial intelligence, Digital Transformation, digital transformation week, dtw, dtw europe, Internet of Things, IoT, jaap van vliet

Jaap van Vliet, Ambit Software: Navigating the intricacies of digital transformation

In an interview at this year’s Digital Transformation Week Europe, Ambit Software Managing Director Jaap van Vliet delved into the intricacies of digital transformation and how the company utilises innovative strategies to assist clients globally. “We try to help to accelerate our customers to improve their business,” says van Vliet. “We do that from multi-angles:… Read more »

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a3 vm, Artificial intelligence, Cloud computing, Digital Transformation, gke, Google Cloud, inference, jax, Kubernetes, kubernetes engine, llm, Platforms, tensor core, tensorflow, tpu v5, tpu v5e

Google Cloud unveils AI-optimised infrastructure enhancements

Google Cloud has announced significant advancements in its AI-optimised infrastructure, including fifth-generation TPUs and A3 VMs based on NVIDIA H100 GPUs. Traditional approaches to designing and constructing computing systems are proving inadequate for the surging demands of workloads like generative AI and large language models (LLMs). Over the last five years, the parameters in LLMs… Read more »

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AI, Artificial intelligence, News & Analysis

AI is getting there but still confusing…

Robotic hand, accessing on laptop, the virtual world of information. Concept of artificial intelligence and replacement of humans by machines.Research from Narrative Science claims confusion over the definition of artificial intelligence is holding it back, although 62% of enterprise respondents believe it will be place by 2018, reports Telecoms.com.

Although this is an encouraging statistic, the report also highlights there is confusion over the definition of the technology itself. 62% of those who contributed to the survey said they were not using AI currently, however later in the survey 88% of the same people were then found to be products or solutions which are under pinned by AI technology. 20% of the respondents highlighted AI wouldn’t be implemented in their organization until there was more clarity on what the technology is, where it fits into the IT function and what the benefits are.

These statistics more than anything else highlight confusion, and ignorance to the artificial intelligence technology which is already present in their day-to-day lives. AI isn’t new, in science fiction movies or in real life. From Siri on Apple devices to Amazon’s recommended purchases or Facebook’s content recommendations, AI has been drip feed into the real-world of technology with few people realizing its impact. The functions mentioned are AI at one of its simplest versions, though IBM has been making progress with its Watson offering moving into more complex arenas, such as medical diagnosis, building management and weather modelling systems.

But what is the real potential of artificial intelligence? According to the report, predictive analytics is the most prominent use-case. 38% of the respondents believe prediction on activity relating to machines, customers or business health is the most relevant use-case. This is one of the more obvious use-cases as there is a direct link to the bottom line, recouping the investment made in the technologies. Whether this is repairs on leased equipment, understanding which customers are most likely to churn or understanding external factors which may impact the supply/demand dynamic, these are all use-cases which impact the bottom line.

These use-cases can also be linked back to the growth of big data and the desire to become more competitive by being more intelligent. The more information a company has access to, the more well-informed decisions become and the risk undertaken is reduced. Dependent on who you speak to the industry is either very good or very bad at using data. The number is almost certainly in the middle, as there is only so many man hours which can be contributed towards the analysis of this data, and data scientists are in-demand.

With the introduction of IoT, increased efficiency in collection and more effective real-time solutions, the tidal wave of information available to an organization will continue to grow. For the investment in data collection, storage and management to be realized, an artificially intelligent solution to comprehend the information and turn it into insight is an alternative, as a human could not stay awake long enough to do the same level of work. To ensure ROI and avoid drowning in the swell of information, artificial intelligence could be critical.

Another area which received attention during the report was automation. This would appear to be low on the agenda currently, though 25% of the respondents felt this was the most important use-case moving forward. One of the myths which have been swirling around artificial intelligence since the release of Terminator is the idea AI will eventually remove the requirement for humans. It’s all very doom and gloom, however AI offers companies the opportunity to take the more mundane, simplistic and repetitive tasks away from employees, to ensure they can focus more time on what would be considered more valuable and critical to the success of the business.

While there still needs to be a focus around what artificial intelligence actually is and what can be achieved through the implementation of such next gen technologies, progress is beginning to be seen. Should cloud computing and 5G be the driving forces towards IoT, to ensure the time and investment is not a waste, assistance from AI driven solutions would appear to be crucial. An AI solution will not (or at least in the near future) make business critical decisions, though the promise of big data is to provide a suitable level of information to ensure businesses are making informed decisions. AI could be the link between information and insight.

AI, Artificial intelligence, data centre, Intel, IoT, News & Analysis

Intel digs deep into wallet to buy its way into AI game

AI-Artificial-Intelligence-Machine-Learning-Cognitive-ComputingVirtual reality may well have been capturing the imagination of the industry in recent months, but Intel’s $400 million of AI start-up Nervana highlights it’s not all fun and games, reports Telecoms.com.

Having set its position as a leader in the data centre market and then largely missed out on the smartphone revolution, it would appear Intel is determined not to miss out on the burgeoning IoT segment, with the Nervana purchase added more firepower to the company’s efforts. The acquisition also highlights the importance of artificial intelligence to the development of the technology industry.

“Intel is a company that powers the cloud and billions of smart, connected computing devices,” said Diane Bryant, GM of the Data Center Group at Intel. “Thanks to the pervasive reach of cloud computing, the ever decreasing cost of compute enabled by Moore’s Law, and the increasing availability of connectivity, these connected devices are generating millions of terabytes of data every single day. The ability to analyse and derive value from that data is one of the most exciting opportunities for us all. Central to that opportunity is artificial intelligence.”

The IoT revolution is coming whether we like it or not, and with it will come such vast amounts of data. Due to the volume, it will beyond comprehension for humans to develop insight from the information. Current data analytics tools and processes could be described (at best) as adequate, though this is before the surge in connected devices. Statista estimates the number of connected devices will grow from 18.2 billion in 2015, through to 50.2 billion in 2020. The devices themselves will also improve, increasing the amount of information which can be collected individually, which will lead to a tidal wave of data to be analysed.

If it is assumed to be immensely difficult or more likely impossible to analyse this data and turn it into actionable insight, what is the point in collecting it in the first place. This is the justification of artificial intelligence. Using such technologies to undertake more rudimentary decision making capabilities brought about through data analysis, or presenting insight to the more complex decisions to business leaders, is where the value of artificial intelligence will be felt. If cloud computing enables the IoT revolution, artificial intelligence will make sure it’s not a waste of time or money.

For a notable proportion of the population, AI is likened to Terminator or other such doomsday stories. But as Bryant notes below, the applications of AI will stretch throughout the life of a consumer, but perhaps more importantly, the business, manufacturing and services world.

“While artificial intelligence is often equated with great science fiction, it isn’t relegated to novels and movies,” said Bryant. “AI is all around us, from the commonplace (talk-to-text, photo tagging, and fraud detection) to the cutting edge (precision medicine, injury prediction, autonomous cars). Encompassing compute methods like advanced data analytics, computer vision, natural language processing and machine learning, artificial intelligence is transforming the way businesses operate and how people engage with the world.”

The acquisition does answer a question raised by Telecoms.com a couple of weeks ago. During early July, Intel announced a new initiative with BMW and Mobileye to drive forward the development of autonomous vehicles. The initiative showed potential, though should BMW are to supply the cars, Intel the chips and Mobileye the detection capabilities, have the body, the muscles and the eyes, but not the brain/AI to bring it all together. This Nervana acquisition in theory completes the circle and provides the intelligence aspect of the car.

Artificial intelligence has the potential to shape the technology industry moving forward, and it would appear this is a view which is shared by the major players. Google has acquired nine AI firms, including Deepmind for $625 million, Twitter has four major acquisitions, most recently Magic Pony for $150 million, Salesforce has acquired two AI start-ups already this year and Apple reported bought Turi for $200 million. The money being spent to gain the upper hand in this sub-sector is beginning to rival the early days of cloud computing.

Artificial intelligence, Facebook, Financial Results, mobile advertising, News & Analysis

Facebook puts Twitter into crosshair

FacebookFacebook has reported healthy growth in advertising revenues over the last quarter, and also outlined its ambitions to take on Twitter and traditional search engine, reports Telecoms.com.

Speaking on the company’s quarterly earnings call, CEO Mark Zuckerberg highlighted his intentions to expand the boundaries of Facebook, mounting a challenge to conversation platform Twitter, building search capabilities for businesses on the social network, as well as directing notable investment into video capabilities. Advertising revenues across the period increased 63% year-on-year to $6.2 billion, with mobile accounting for 84%, though the team are seemingly not satisfied as it prepares to venture into new markets.

“We have a saying at Facebook that our journey is only 1% done,” said Zuckerberg. “And while I’m happy with our progress, we have a lot more work to do to grow our community and connect the whole world. That means making big investments and taking risks, focusing not just on what Facebook is but on what it can be.”

While Twitter was not mentioned specifically during the call, Zuckerberg outlined his intentions to make Facebook the platform where users search for and express their views on current affairs. Most users would currently search for and post updates to their immediate circle of contacts, though this is an aspect which Zuckerberg wants to change. When looking at keyword searches, the team claim there are now 2 billion searches per day of its 2.5 trillion posts, growing 33% over the last nine months. The challenges towards Twitter was not implicitly mentioned by the Facebook chief, though the team are seemingly on a mission to create a conversation platform which extends beyond an users immediate circle, a space which has been dominated by Twitter in recent years.

Another area of potential growth for Facebook is commercial search. According to Zuckerberg, over a third of small and medium businesses in the states do not have a website, though Facebook could provide an alternative. Setting up and managing a website can be challenging, as well as for those who are less technically able, the team are promoting the use of the platform to create company pages and build the online presence of these organizations through Facebook. Although this is a long-term ambition, and the team are not in a stage where notable revenues are realistic, it would appear to be a move to provide an alternative to traditional search engines.

“This is why Facebook pages are the mobile solutions for many of the 60 million businesses using our products each month in the U.S. and around the world,” said Zuckerberg. “We’ve made it easy for business owners to manage their Facebook page from their mobile device. Over 85% of active business pages use mobile, and 40% of active advertisers have created a Facebook ad on their mobile device.

“So when we talk about our strategy (commercial search), I often talk about how when we develop new products we think about it in three phases. First, building a consumer use case; then, second, making it so that people can organically interact with businesses; and then third, on top of that, once there’s a large volume of people interacting with businesses. Give businesses tools to reach more people and pay, and that’s ultimately the business opportunity.

“I’d say, we’re around the second phase of that in search now.”

Artificial intelligence is another area which ties into the commercial capabilities of search, as once AI is perfected by the team, it does offer the opportunity to dramatically increase the relevance of ads put in front of the consumer. While most adverts are placed on historical data and previous customer behaviour, the potential of AI is intuition, the ability to make human decisions on what a potential customer would be interested it. This quarter, Facebook announced the development of DeepText, a deep learning based engine that can understand the context of several thousand posts per second across 20 different languages. It’s the beginning of the move towards AI, but a promising start.

As with other social media brands, video has been outlined as a priority for the team, building on the theme of consumer trends towards mobile. Most recently Facebook has been focused on the implementation of live video, though Zuckerberg highlighted the team will continue to invest in video platforms, to capitalize on the growing role of video in social media.

“We’re also working on new tools to help people express themselves and understand what’s going on with the people they care about. Ten years ago, most of what we shared and consumed online was text. Now its photos, and soon most of it will be video. We see a world that is video first with video at the heart of all of our apps and service.”

The shift towards mobile is fast changing the way customers consume and interact with media, most notably video. Before the phenomena of video can be capitalized on, the right capabilities need to be in place, and firstly this means investment.

All-in-all, most people would comment this has been a successful quarter for the social media giant. Total revenues are up to $6.4 billion, a 59% increase, daily active users standing at 1.13 billion on average for June 2016, an increase of 17% year-over-year, and monthly active users at 1.71 billion as of June 30, 2016, an increase of 15% year-over-year. Facebook has arguably been the most successful company at capitalizing on its captured audience, and should it effectively build capabilities in the conversation and search segments, it will be a worrying sign for Twitter and more traditional search engines.

AI, Artificial intelligence, Google, Image Recognition, News & Analysis

Google adds image recognition to growing AI portfolio

Googlers having funGoogle has continued its charge on the artificial intelligence market through purchasing French image recognition startup Moodstocks, reports Telecoms.com.

Moodstocks, founded in 2008, develops machine-learning based image recognition technology for smartphones, which has been described by developers as the ‘Shazam for images’. Financials of the agreement have not been confirmed to date.

“Ever since we started Moodstocks, our dream has been to give eyes to machines by turning cameras into smart sensors able to make sense of their surroundings,” Moodstock said on its website. “Today, we’re thrilled to announce that we’ve reached an agreement to join forces with Google in order to deploy our work at scale. We expect the acquisition to be completed in the next few weeks.”

Artificial intelligence is one of the focal points of the Google strategy moving forward, which was confirmed by Google CEO Sundar Pichai during the company’s recent earnings call, though the focus can be dated back to the $625 million DeepMind acquisition in 2014. Although DeepMind is arguably the most advanced AI system in the industry, Telecoms.com readers recently confirmed in a poll Google was the leader in the AI segment, it has seemingly been playing catch up with the likes of Watson and AWS whose offerings have been in the public eye for a substantially longer period of time.

The recognition tools are most likely to be incorporated into the Android operating system, though Moodstocks customers will be able to continue to use the service until the end of their subscription. Moodstocks will be incorporated into Google’s R&D centre in France, where the team will work alongside engineers who are focusing on the development of Youtube and Chrome, two offerings where there could be a link to the Moodstocks technology.

“Many Google services use machine learning (or machine learning) to make them simpler and more useful in everyday life – such as Google Translate, Smart Reply Inbox, or the Google app,” said Vincent Simonet, Head of R&D centre of Google’s French unit. “We have made great strides in terms of visual recognition: now you can search in Google Pictures such as ‘party’ or ‘beach’ and the application will offer you good pictures without you and have never needed to categorize them manually.”

Last month, Google also announced it was expanding its machine research team by opening a dedicated office in Zurich. The team will focus on three areas specifically, machine intelligence, natural language processing & understanding, as well as machine perception.

Elsewhere in the industry, Twitter completed the acquisition of Magic Pony last month reportedly for $150 million. Magic Pony, which offers visual processing technology, was one of the more public moves made by the social media network, which could be seen as unusual as the platform lends itself well to the implementation of AI. Microsoft also announced the purchase of Wand Labs, building on the ‘Conversation-as-a-Platform’ proposition put forward by CEO Satya Nadella at Build 2016.

AI, Android, Artificial intelligence, Google, Machine learning, News & Analysis

Image recognition startup joins Google in France

Googlers having funGoogle has continued its charge on the artificial intelligence market through purchasing French image recognition startup Moodstocks, reports Telecoms.com.

Moodstocks, founded in 2008, develops machine-learning based image recognition technology for smartphones, which has been described by developers as the ‘Shazam for images’. Financials of the agreement have not been confirmed to date.

“Ever since we started Moodstocks, our dream has been to give eyes to machines by turning cameras into smart sensors able to make sense of their surroundings,” Moodstock said on its website. “Today, we’re thrilled to announce that we’ve reached an agreement to join forces with Google in order to deploy our work at scale. We expect the acquisition to be completed in the next few weeks.”

Artificial intelligence is one of the focal points of the Google strategy moving forward, which was confirmed by Google CEO Sundar Pichai during the company’s recent earnings call, though the focus can be dated back to the $625 million DeepMind acquisition in 2014. Although DeepMind is arguably the most advanced AI system in the industry, Telecoms.com readers recently confirmed in a poll Google was the leader in the AI segment, it has seemingly been playing catch up with the likes of Watson and AWS whose offerings have been in the public eye for a substantially longer period of time.

The recognition tools are most likely to be incorporated into the Android operating system, though Moodstocks customers will be able to continue to use the service until the end of their subscription. Moodstocks will be incorporated into Google’s R&D centre in France, where the team will work alongside engineers who are focusing on the development of Youtube and Chrome, two offerings where there could be a link to the Moodstocks technology.

“Many Google services use machine learning (or machine learning) to make them simpler and more useful in everyday life – such as Google Translate, Smart Reply Inbox, or the Google app,” said Vincent Simonet, Head of R&D centre of Google’s French unit. “We have made great strides in terms of visual recognition: now you can search in Google Pictures such as ‘party’ or ‘beach’ and the application will offer you good pictures without you and have never needed to categorize them manually.”

Last month, Google also announced it was expanding its machine research team by opening a dedicated office in Zurich. The team will focus on three areas specifically, machine intelligence, natural language processing & understanding, as well as machine perception.

Elsewhere in the industry, Twitter completed the acquisition of Magic Pony last month reportedly for $150 million. Magic Pony, which offers visual processing technology, was one of the more public moves made by the social media network, which could be seen as unusual as the platform lends itself well to the implementation of AI. Microsoft also announced the purchase of Wand Labs, building on the ‘Conversation-as-a-Platform’ proposition put forward by CEO Satya Nadella at Build 2016.

Artificial intelligence, Ericsson, Internet of Things, Machine Vision, News & Analysis, vodafone

Machine Vision 5G use case demonstrated by Ericsson and Vodafone

Engine manufactoringEricsson and Vodafone have successfully demonstrated another 5G Proof of Concept, this time focusing on Machine Vision (MV) application, reports Telecoms.com.

The team created a 5G Smart Network Edge prototype including a 5G ready core and demonstrated the benefits of network slicing and distributed cloud technology for MV. Making the announcement at the Innovation Days at Ericsson’s R&D Center in Aachen, the team demonstrated how the 5G Smart Network Edge enables much greater efficiency for industry. Due to reduced network latencies the recognition rate of a cloud-based face detection application was increased. The PoC also confirmed data could be stored locally, decreasing the risk of breaches, loss or unauthorized access.

“Within only 3 months we created a 5G Smart Network Edge prototype by connecting our labs,” Sonja Graf, Head of Vodafone Innovation Park at Vodafone Germany. “The Face Recognition use case is just one example demonstrating how 5G will meet the diverse needs of a wide range of industries.”

While MV is not a new concept for the industry, it is becoming increasing commonplace for quality assurance, inspection and industrial robot guidance processes in the manufacturing industry. Examples of MV include wood quality inspection, robot guidance and checking orientation of components and reading of serial numbers. Actions of the back of the inspection can be automated, opening up the door for artificial intelligence in the manufacturing industry.

“We are delighted that the Ericsson and Vodafone labs have come together to innovate and this first use case shows an excellent example of how 5G can enable industries to become more efficient as well as more secure and cost effective,” said Valter D’Avino, Head of Ericsson Western & Central Europe.

AI, Artificial intelligence, Machine learning, News & Analysis, Twitter

Twitter acquires machine learning start-up Magic Pony

Twitter has stepped up its efforts in the machine learning arena after announcing the acquisition of visual processing technology company Magic Pony.

While the company claims machine learning is central to the brands capabilities, it has been relatively quiet in the market segment in comparison to industry heavy weights such as IBM, Google and Microsoft. This is the third acquisition the team has made in this area, reported to be in the range of $150 million, following the purchase of Whetlab last year and Mad Bits in 2014, compared to Google who acquired Jetpac, Dark Blue Labs and Vision Factory, as well as $500 million on DeepMind, all in 2014.

“Machine learning is increasingly at the core of everything we build at Twitter,” said Jack Dorsey, Twitter CEO. “Magic Pony’s machine learning technology will help us build strength into our deep learning teams with world-class talent, so Twitter can continue to be the best place to see what’s happening and why it matters, first. We value deep learning research to help make our world better, and we will keep doing our part to share our work and learnings with the community.”

The acquisition follows Twitter’s announcement last week advertisers will now be able to utilize emoji keyword targeting for Twitter Ads. Although a simple proposition in the first instance, the new features did open up the opportunity for machine learning enhanced advertising solutions.

Magic Pony, which was founded in 2014 and currently has 11 employees, was acquired to bolster the visual experiences that are delivered across Twitter apps. The team will link up with Twitter Cortex, the in-house machine learning department, to improve image processing expertise.

The technology itself makes use of the abilities of convolutional neural networks to scale-out an image. By taking the information in a picture, the technology imagines a larger and more in-depth image by scaling out the detail which it sees. Much in the same way a human can imagine the rest of a car by seeing the door, the technology learns lessons from previous experiences and applies logical decisions moving forward.

Magic Pony itself was initially supported by investment from Octopus Ventures who have seemingly found a specialty in finding promising AI start-ups. Prior to Magic Pony being acquired by Twitter, Octopus Ventures invested it Evi which was acquired by Amazon in 2012, and SwiftKey which was acquired by Microsoft this year.

“Today marks a great day for the Magic Pony team,” said Luke Hakes, Investment Director at Octopus Ventures. “We’re proud to have believed in the concept early on and to then have had the privilege of joining their journey. The technology Magic Pony has developed is revolutionary and pushes the boundaries of what is possible with AI in the video space.

“The UK continues to grow as the ‘go-to’ place for companies looking to build best in breed AI technology – Octopus has been fortunate to work with the founders of three companies in this space that have gone on to be acquired, with Evi and Amazon, SwiftKey and Microsoft, and now Magic Pony and Twitter. We are excited for the Magic Pony team, but also to take what we have learnt on the last three journeys and help the next generation of entrepreneurs lead the way in the on-going AI revolution.”

AI, Artificial intelligence, Google, Machine learning, Microsoft, News & Analysis, Twitter

Machine learning front and centre of R&D for Microsoft and Google

Dear Future Im Ready, message on paper, smart phone and coffee on tableMicrosoft and Google have announced plans to expand their machine learning capabilities, through acquisition and new research offices respectively, reports Telecoms.com.

Building on the ‘Conversation-as-a-Platform’ proposition put forward by CEO Satya Nadella at Build 2016, the Microsoft team has announced plans to acquire Wand Labs. The purchase will add weight to the ‘Conversation-as-a-Platform’ strategy, as well as supporting innovation ambitions for Bing intelligence.

“Wand Labs’ technology and talent will strengthen our position in the emerging era of conversational intelligence, where we bring together the power of human language with advanced machine intelligence,” said David Ku, Corporate Vice President of the Information Platform Group on the company’s official blog. “It builds on and extends the power of the Bing, Microsoft Azure, Office 365 and Windows platforms to empower developers everywhere.”

More specifically, Wand Labs adds expertise in semantic ontologies, services mapping, third-party developer integration and conversational interfaces, to the Microsoft engineering team. The ambition of the overarching project is to make the customers experience more seamless by harnessing human language in an artificial environment.

Microsoft’s move into the world of artificial intelligence and machine learning has not been a smooth ride to date, though this has not seemed to hinder investment. Back in March, the company’s AI inspired Twitter account Tay went into melt-down mode, though the team pushed forward, updating its Cortana Intelligence Suite and releasing its Skype Bot Platform. Nadella has repeatedly highlighted artificial intelligence and machine learning is the future for the company, stating at Build 2016:

“As an industry, we are on the cusp of a new frontier that pairs the power of natural human language with advanced machine intelligence. At Microsoft, we call this Conversation-as-a-Platform, and it builds on and extends the power of the Microsoft Azure, Office 365 and Windows platforms to empower developers everywhere.”

Google’s efforts in the machine learning world have also been pushed forward this week, as the team announced dedicated machine learning research based in the Zurich offices, on its blog. The team will focus on three areas specifically, machine intelligence, natural language processing & understanding, as well as machine perception.

Like Microsoft, Google has prioritized artificial intelligence and machine learning, though both companies will be playing catch-up with the likes of IBM and AWS, whose AI propositions have been in the market for some time. Back in April, Google CEO Sundar Pichai said in the company’s earnings call “overall, I do think in the long run, I think we will evolve in computing from a mobile first to an AI first world,” outlining the ambitions of the team.

Google itself already has a number of machine learning capabilities incorporated in its product portfolio, those these could be considered as relatively rudimentary. Translate, Photo Search and SmartReply for Inbox already contains aspects of machine learning, though the team are targeting more complex and accurate competencies.

Elsewhere, Twitter has announced on their blog advertisers will now be able to utilize emoji keyword targeting for Twitter Ads. This new feature uses emoji activity as a signal of a person’s mood or mind set, allowing advertisers to more effectively communicate marketing messages minimizing the potential for backlash of disgruntled twitter users. Although the blog does not state the use of machine learning competencies, it does leave the opportunity for future innovation in the area.